Wireless power transfer system analyzer

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for analyzing a wireless power transfer system are disclosed. In one aspect, a method includes the actions of receiving data related to the energy usage of an electric vehicle. The actions further include, based on the data related to the energy usage of the electric vehicle, determining that a problem exists with the electric vehicle. The actions further include, in response to determining that that the problem exists with the electric vehicle, accessing diagnostic data of the electric vehicle. The actions further include, based on the diagnostic data of the electric vehicle and the data related to the energy usage of the electric vehicle, determining a cause of the problem with the electric vehicle. The actions further include providing, for output, data indicating the cause of the problem with the electric vehicle.

BACKGROUND

Wireless power transfer is the transfer of electrical power withoutwires as a physical link. A wireless power transfer system includes atransmitter device and a receiver device. The transmitter device isdriven by a power source and generates an electromagnetic field. Thereceiver device extracts power from the electromagnetic field andsupplies the power to an electrical load.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures, in which the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items.

FIG. 1 illustrates an example wireless power transfer system that isconfigured to determine the cause of an issue with the wireless powertransfer system and/or an electric vehicle.

FIG. 2 illustrates an example server that is configured to determine thecause of an issue with a wireless power transfer system and/or anelectric vehicle.

FIG. 3 illustrates an example vehicle that is configured to determinethe cause of an issue with the electric vehicle.

FIG. 4 is a flowchart of an example process for determining the cause ofan issue with a wireless power transfer system or electric vehicle.

DETAILED DESCRIPTION

Electric vehicles can exhibit various problems. For some problems, thecause and corresponding solution may be obvious. For example, if avehicle interior is inadequately lit, the cause may be a burned-outlight bulb. The solution may be to replace the burned-out light bulb.Other problems may not have obvious solutions. For example, if thevehicle is using more battery power than it should for a distancetraveled, then the cause may include a bad battery, a bad powerreceiving device, a bad electric motor, and/or other causes. In someinstances, it may not be apparent that the vehicle has a problem. Thismay be the case if the vehicle does not exhibit any observable symptoms.

Because of this difficulty with identifying the causes and correspondingsolutions to problems with electric vehicles, electric vehicles andcharging devices may be equipped with various sensors that provide datato a server for analysis. The server may use various rules and/or modelsto analyze the sensor data. The rules may specify ranges and thresholdsto compare to the sensor data. If the sensor data satisfies some of theranges or thresholds, then the server may determine that a particularproblem with the vehicle likely exists. The models may be trained usinghistorical data and machine learning. In some instances, the models maybe configured to receive the sensor data and output that a particularproblem with the vehicle likely exists.

The server may continue to analyze the sensor data in view of anidentified problem. This analysis may involve using additional modelsand/or rules. The additional models and/or rules may identify the likelycause and/or solution to the problem. In some instances, the server mayautomatically implement the solution to attempt to correct the problem.In other instances, the server may output instructions to implement thesolution.

FIG. 1 illustrates an example wireless power transfer system 100 that isconfigured to determine the cause of an issue with the wireless powertransfer system 100 and/or an electric vehicle 104. Briefly, and asdescribed in more detail below, the wireless power transfer system 100includes a charging pad 110 that is configured to provide powerwirelessly to the electric vehicle 104. The electric vehicle 104 mayinclude vehicle sensors 116 that collect vehicle sensor data 144 relatedto the electric vehicle 104. The charging pad 110 and the associatedcharger circuitry 112 may communicate with charger sensors 114 thatcollect charger sensor data 142 related to the charging pad 110 and theassociated charger circuitry 112. A server 106 may receive and analyzethe vehicle sensor data 144 and the charger sensor data 142 and identifyany problems related to the wireless power transfer system 100 and/orthe electric vehicle 104. The server 106 may determine a cause andsolution of the problem. The server 106 may automatically implement thesolution. In some implementations and as discussed below with respect toFIG. 3 , the vehicle 104 may be configured to determine the cause andsolution of the problem and automatically implement the solution. FIG. 1includes various stages A through H that may illustrate the performanceof actions and/or the movement of data between various components of thewireless power transfer system 100. The wireless power transfer system100 may perform these stages in any order.

In more detail, the user 102 may be operating the vehicle 104. Thevehicle 104 may be any type of motorized vehicle such as a car, truck,van, bus, train, motorcycle, electric bicycle, scooter, tractor, drayagetruck, street sweeper, watercraft, electric vertical take-off andlanding (eVTOL) aircraft, or any other similar type of vehicle. In someimplementations, the vehicle 104 may be any type of device that includesa motor and a battery such as a lawnmower, tiller, generator, snowblower, and/or any other similar type of device.

The vehicle 104 may include a receiving pad 122, a battery 118, andassociated receiver circuitry 123. The receiving pad 122 may beconfigured to receive power wirelessly from a charging pad 110. Thecharging pad 110 may receive power from the charger circuitry 112 thatreceives power from the power grid 111. The receiver circuitry 123 mayinclude converters, inverters, and/or control circuitry to transferpower from the receiving pad 122 to the battery 118. The charging pad110 and the receiving pad 122 may include coils that are configured towirelessly couple together at a resonant frequency during the transferof the wireless power 140. The charging pad 110 and the receiving pad122 may also include magnetic material and/or metal in order to improvethe transfer of the wireless power 140 and to prevent the wireless power140 from affecting or being affected by nearby people, animals, and/ordevices. The coils of the charging pad 110 and the receiving pad 122 mayresonate at the resonant frequency. At the resonant frequency, thetransfer of the wireless power 140 may be more efficient than at otherfrequencies. In some implementations, the charging pad 110 may beconfigured to transfer power to the receiving pad 122 without couplingtogether at the resonant frequency. In some implementations, the vehicle104 may provide power to various electronic devices such as mobilephones, cameras, power converters, and/or any other similar type ofdevice. These devices may draw power from the battery 118.

The power grid 111 may be operated by a utility company. The utilitycompany may operate a power plant and use transmission lines to deliverpower to the charger circuitry 112. The power plant may generateelectricity using coal, natural gas, solar, wind, water, and/or anyother renewable or nonrenewable source. In some implementations, thecharger circuitry 112 may be connected a power source such as solarpanels. In this case, the charger circuitry 112 may not be connected tothe power grid of a utility company and may receive its power from solarpanels that may be in the vicinity of the charger circuitry 112. Forexample, the solar panels may be on top of a car port that may cover thevehicle 104 when the vehicle 104 is receiving power from the chargingpad 110.

The receiving pad 122 may receive the wireless power 140 from thecharging pad 110. The charging pad 110 may receive power from thecharger circuitry 112. The charger circuitry 112 may include variouspower conversion, power inversion, and/or control circuitry thattransfers power from the power grid 111 to the charging pad 110. Thecharging pad 110 and charger circuitry 112 may be configured to transferpower at various rates, such as eleven, fifty, one hundred, and/or fivehundred kilowatts. In the example of FIG. 1 and in stage A, the chargingpad 110 may transfer twenty kilowatt-hours of wireless power to thereceiving pad 122. The receiving pad 122 may provide the wireless power140 as alternating current power to a converter of the receivercircuitry 123 that converts the alternating current power of thewireless power 140 to direct current power. The charging pad 110 mayprovide the twenty kilowatt-hours of wireless power to the converter ofthe receiver circuitry 123 less some energy that may be lost as heat,leakage, rectification, or through other inefficiencies in the vehicle104. The converter of the receiver circuitry 123 may store theapproximately twenty kilowatt-hours of wireless power in the battery118.

The charger sensors 114 may be configured to collect charger sensor data142 that reflects characteristics and operations of the charging pad 110and/or the charger circuitry 112. The charger sensors 114 may includevarious types of sensors such as power meters that measure the powerprovided by the power grid 111 to the charger circuitry 112, powerprovided by the charger circuitry 112 to the charging pad 110, and powerprovided by the charging pad 110 for receipt by the receiving pad 122.The power meters may also measure power provided to other vehicles. Thecharger sensors 114 may include thermometers that measure the ambienttemperature and/or the temperature of any component of the charging pad110 and/or the charger circuitry 112. The charger sensors 114 may alsoinclude location sensors that determine the location of the charging pad110 and/or the charger circuitry 112, a hygrometer that measures themoisture content of the ambient air and/or the air inside any componentof the charging pad 110 and/or the charger circuitry 112 such as insidethe charging pad 110, and/or a water sensor that may detect the presenceof water in and/or around the charging pad 110 and/or the chargercircuitry 112, to name just some examples. The charger sensors 114 mayalso include alignment sensors that detect an orientation between thecharging pad 110 and the receiving pad 122. For example, an alignmentsensor may determine that the centers of the charging pad 110 and thereceiving pad 122 are offset by three inches and/or that the chargingpad 110 and the receiving pad 122 are seven inches apart. In addition,the charger sensors 114 may timestamp the charger sensor data 142. Thetimestamps may indicate a date and time at which a sensor detected acertain condition and may also indicate the charging activity of thecharging pad 110. For example, the timestamps may indicate that thecharging pad 110, which may be capable of outputting eleven kilowatts,was outputting seven kilowatts at a first time and for a period afterthe first time and outputting ten kilowatts at a second time and for aperiod after the second time.

As illustrated in FIG. 1 and in stage B, the charger sensors 114 mayprovide the charger sensor data 142 to the server 106. For example, thecharger sensors 114 may provide the charger sensor data 142 continuouslyor at intervals during charging of a vehicle, or over a period of timeat periodic intervals such as every hour. The charger sensors 114 mayprovide the charger sensor data 142 to the server 106 during or aftercharging the vehicle 104 or any other vehicle, for example, in realtime, or at any other time. In some embodiments, the charger sensor data142 may indicate the amount of power (e.g., 20 kWhr) transferred by thecharging pad 110 to the receiving pad 122 of the vehicle 104.

The vehicle sensors 116 may be configured to collect vehicle sensor data144 that reflects characteristics and operations of the vehicle 104. Forexample, the vehicle sensors 116 may include an odometer that measuresthe number of miles driven by the vehicle 104 and/or the number of milesdriven by the vehicle 104 since the last charge from the charging pad110 or another similar charging device. The vehicle sensors 116 may alsoinclude a location sensor that determines the location of the vehicle104, vehicle accessory monitors that may monitor the usage of variousaccessories of the vehicle 104 (such as headlights, interior lights, airconditioning systems, heating systems, audio recording and outputsystems, video recording and output systems, automatic door operators,and/or any other similar vehicle accessory), battery level monitors thatmeasure the capacity of the battery 118 and the remaining power left inthe battery 118, a voltmeter that measures the voltage of the battery118, various thermometers that measure the temperature of variouscomponents of the vehicle 104 (for example, the temperature of variousportions of the battery 118, various portions of the motor, the ambienttemperature, and any other similar locations), a hygrometer thatmeasures the moisture content of the ambient air and/or the air insideany component of the vehicle 104, a water sensor that may detect thepresence of water in and/or around any component of the vehicle 104including the receiving pad 122, etc. The vehicle sensors 116 also maytimestamp the vehicle sensor data 144. The timestamps may indicate adate and time at which a sensor detected a certain condition.

The vehicle 104 may include a brake energy recoverer 124 that is part ofa regenerative braking system. The brake energy recoverer 124 may be acomponent that slows down (i.e., decelerates) the vehicle 104 andconverts kinetic energy of the vehicle 104 into energy that can bestored in the battery 118. The vehicle 104 may also include a typicalbraking system that slows the vehicle using disk brakes and/or drumbrakes. The brake energy recoverer 124 may operate according to theregenerative braking profile 120. The regenerative braking profile 120may specify a time and/or location to activate the brake energyrecoverer 124. More specifically, the regenerative braking profile 120may specify to activate the brake energy recoverer 124 to decelerate theelectric vehicle 104 based on a distance between the electric vehicle104 and a predetermined location. For example, if the vehicle 104 is abus and is approaching a bus stop, then the regenerative braking profile120 may specify to slow the vehicle 104 as the vehicle 104 approachesthe bus stop. In some implementations, the regenerative braking profile120 may specify how the brake energy recoverer 124 works in conjunctionwith the brake foot pedal with which the user 102 interacts. Thiscooperation between the brake energy recoverer 124 and the brake footpedal may be related to the location of the vehicle 104. For example, ifthe vehicle 104 is within a certain range of or within a thresholddistance from a bus stop based on a predetermined bus route and locationdata and the user 102 presses the brake foot pedal, then theregenerative braking profile 120 may specify to activate the brakeenergy recoverer 124. If the vehicle 104 is closer to the bus stop thanthe lower end of the range, then the regenerative braking profile 120may include instructions not to activate the brake energy recoverer 124or not include any instructions for that scenario. In someimplementations, the regenerative braking profile 120 may includeinstructions to activate the brake energy recoverer 124 based on anamount of pressure applied to the brake foot pedal. Pressure above acertain threshold may indicate that the user 102 is attempting to stopthe vehicle 104 quickly. In this case, the regenerative braking profile120 may specify to not activate the brake energy recoverer 124. Pressurebelow a certain threshold may indicate that the user 102 is attemptingto stop the vehicle 104 slowly. In this case, the regenerative brakingprofile 120 may specify to activate the brake energy recoverer 124. Thebrake energy recoverer 124 may be configured to decelerate the vehicle104 slowly. Increased pressure on the brake pedal may be an indicationthat the user 102 is attempting to slow the vehicle 104 quickly. In thiscase, the conventional brakes may engage causing most of the kineticenergy of the vehicle 104 to be lost as heat instead of captured by thebrake energy recoverer 124.

The regenerative braking profile 120 may be specific to the drivinghabits of the user 102. The regenerative braking profile 120 may specifyto activate the brake energy recoverer 124 more often while notcompromising the safety of the vehicle 104. Because different users mayuse the brakes differently in different situations, the regenerativebraking profile 120 may be tailored to take into account the brake usageof different users. In some instances, the regenerative braking profile120 may be route dependent. In the case of busses, one regenerativebraking profile 120 may be used when the user 102 is driving a first busroute and another regenerative braking profile 120 may be used when theuser 102 is driving a second bus route. Each user may have a differentregenerative braking profile for the same bus route.

The vehicle 104 may receive the regenerative braking profiles 120 fromvarious sources. For example, vehicle 104 may be preloaded with aregenerative braking profile 120 at the time of manufacture. Thisregenerative braking profile 120 may specify under what situations toengage the brake energy recoverer 124. As another example, the vehicle104 may receive an update to the regenerative braking profile 120 from aserver. This may be in response to an issue with the vehicle 104 and/oran improvement to the regenerative braking profile 120. The regenerativebraking profiles 120 may include multiple profiles. These profiles maybe preloaded during manufacture and/or received and/or updated from aserver at a later time. The vehicle 104 may activate a regenerativebraking profile 120 based in determining an identity of the user 102,the location of the vehicle 104, the type of vehicle 104, the route ofthe vehicle 104, and/or any other similar data.

The vehicle sensors 116 may include a brake energy recoverer monitorthat is configured to monitor the usage of the brake energy recoverer124. The brake energy recoverer monitor may collect data related to thetime periods when the brake energy recoverer 124 is active. The brakeenergy recoverer monitor may also monitor the usage of the conventionalbrakes and the interaction between the user 102 and the brake pedaland/or the gas pedal. The brake energy recoverer monitor may also beconfigured to collect data related to the amount of energy recovered bythe brake energy recoverer 124 and stored in the battery. The datarelated to the brake energy recoverer 124 may be included in the vehiclesensor data 144 and may also be timestamped in order to correlate thedata related to the brake energy recoverer 124 with data from the othervehicle sensors 116.

As illustrated in FIG. 1 and in stage C, the vehicle 104 may provide thevehicle sensor data 144 to the server 106. The vehicle sensor data 144may be provided during charging or at periodic intervals such as everyhour, in response to a request from the server 106, after receivingwireless power 140 from the charging pad 110, or at any other time. Thevehicle sensor data 144 may indicate the distance that the vehicle 104has driven (e.g., one hundred fifty miles), the path that the vehicle104 has been driven (e.g., along Pecan Street and Elm Street), and/orother information (e.g., the interior lights have been on for ten hours,the air conditioning has been used for ten hours, the battery level isthirty percent, three kilowatt hours of power have been recovered fromthe brake energy recoverer 124 and stored in the battery 118, the brakeenergy recoverer 124 has been active for twenty percent of the time thatthe vehicle 104 has been in motion, and the voltage of the battery 118is three hundred volts). The vehicle sensor data 144 may also includevarious battery thermal parameters that indicate the temperature ofvarious cells of the battery 118.

The server 106 may receive and analyze the charger sensor data 142 andthe vehicle sensor data 144. Based on analyzing the charger sensor data142 and the vehicle sensor data 144, the server 106 may be able todetermine whether there is a problem with the vehicle 104, charging pad110, and/or the charger circuitry 112. The server 106 may determine thecause of the problem and a solution. In some implementations, the server106 may output the solution. In some implementations, the server 106 mayautomatically implement the solution.

The server 106 may include sensor data storage 126. The sensor datastorage 126 may be implemented by memory or another storage deviceaccessible by the server 106. The sensor data storage 126 may store thecharger sensor data 142 and the vehicle sensor data 144. The sensor datastorage 126 may differentiate between energy usage data 128 anddiagnostic data 130. The energy usage data 128 may include data relatedto the power supplied by the power grid 111, power supplied by thecharger circuitry 112, energy consumed by the charging pad 110, wirelesspower 140 transferred between the charging pad 110 and the receiving pad122, power transferred from the receiving pad 122 to the battery 118,miles driven, route traveled, battery percentage, and/or any othersimilar data. The diagnostic data 130 may include data related to thesurroundings and characteristics of the vehicle 104, the chargercircuitry 112, and the charging pad 110. For example, the diagnosticdata 130 may include data related to temperatures, accessory usage,battery 118 voltage, brake energy recoverer 124 usage, humidity, waterpresence, and/or any other similar data.

In some implementations, the server 106 may be configured to store datareceived from particular sensors in either the energy usage data 128 orthe diagnostic data 130. For example, the server 106 may store datareceived from thermometers in the diagnostic data 130 and data receivedfrom the power meters to the energy usage data 128. In someimplementations, the sensor data storage 126 may store the chargersensor data 142 and the vehicle sensor data 144 without differentiatingbetween the energy usage data and the diagnostic data.

The server 106 may include an analyzer 132. The analyzer 132 may beconfigured to analyze the sensor data using the problem identifier 134,the cause identifier 136, and the solution identifier 138. The problemidentifier 134 may determine whether there is a problem 146 with thecharging pad 110, the charger circuitry 112, and/or the vehicle 104. Ifthere is a problem, then the cause identifier 136 may determine thecause 148 of the problem 146. The solution identifier 138 may determinethe solution 150 to remove or remedy the cause 148 and correct theproblem 146. In some examples, the server 106 may automaticallyimplement the solution 150. In some examples, the server 106 may outputa recommendation to implement the solution 150.

The problem identifier 134 may analyze the energy usage data 128 and/orthe diagnostic data 130 using various sensor data analysis rules and/orsensor data analysis models. The sensor data analysis rules and sensordata analysis models will be discussed in more detail below. Briefly,the sensor data analysis models may be configured to receive at least aportion of the energy usage data 128 and/or at least a portion of thediagnostic data 130. The sensor data analysis models may be configuredto output data identifying a problem that may likely exist with thevehicle 104, the charging pad 110, and/or the charger circuitry 112. Thesensor data analysis models may also identify a likely problem.Different sensor data analysis models may be configured to analyzedifferent types of data. For example, a first sensor data model may beconfigured to analyze the energy usage data 128 and output dataidentifying a problem with the vehicle 104, the charger circuitry 112,and/or the charging pad 110. A second sensor data model may beconfigured to analyze the energy usage data 128 and diagnostic data 130and output data identifying a problem with the vehicle 104, the chargercircuitry 112, and/or the charging pad 110.

The sensor data rules may specify various ranges and/or thresholds fordifferent types of sensor data. Based on which side of a threshold or onwhich range the value of a portion of the sensor data may be located,the sensor data rules may determine that a specific problem may existwith the vehicle 104, the charger circuitry 112, and/or the charging pad110. Different sensor data rules may include ranges and thresholds fordifferent types of data and may specify different types of problems withthe vehicle 104, the charger circuitry 112, and/or the charging pad 110.In some implementations, the sensor data rules may identify more thanone problem with the vehicle 104, the charger circuitry 112, and/or thecharging pad 110.

In the example of FIG. 1 and in stage D, the problem identifier 134 mayanalyze the energy usage data 128 and/or the diagnostic data 130 usingthe sensor data analysis rules and/or sensor data analysis models. Basedon the data included in the vehicle sensor data 144 and the chargersensor data 142, the problem identifier 134 may select a sensor dataanalysis model that is configured to receive data indicating one or moreof the wireless power provided from the charging pad 110, the milesdriven, the route, the accessory usage, the battery 118 level, the brakeenergy recoverer 124 usage, the battery voltage, and the various batterythermal parameters. The vehicle sensor data 144 and the charger sensordata 142 may include data for each of these fields at various points intime such as the time of charging. The sensor data analysis model mayoutput the problem 146 indicating that the vehicle 104 mileage is toolow for twenty kilowatt hours of energy provided to the vehicle 104 thatused about fifty percent of the power in the battery 118. The problemidentifier 134 may determine that the battery charge increased by fiftypercent from thirty percent to eighty percent when the charging pad 110provided twenty kilowatt hours of power to the vehicle 104. Because thecurrent battery percentage is thirty percent, the problem identifier 134may determine that the vehicle 104 consumed about twenty kilowatt hoursof power from the battery 118. The problem identifier 134 may identify adata analysis rule that indicates a minimum number of miles that thevehicle should travel using twenty kilowatt hours of power. That minimumnumber of miles may be one hundred seventy miles. In this case, theproblem identifier 134 may identify the likely problem 146 being thatthe miles traveled by the vehicle 134 is too low for twenty kilowatthours of energy provided to the vehicle 104.

The cause identifier 136 may be configured to analyze the energy usagedata 128, the diagnostic data 130, and/or the problem 146 using varioussensor data analysis rules and/or sensor data analysis models. In someimplementations, the sensor data analysis rules may specify a cause 148that corresponds to the problem 146. For example, the problem 146 mayindicate that the passenger area of the vehicle 104 is too dark. Asensor data analysis rule may indicate that the cause of this problem isa burned out light bulb. Other problems may not correspond to a precisecause. In this case, the sensor data analysis rules and/or the sensordata analysis models may be used by the cause identifier 136 to analyzethe energy usage data 128, the diagnostic data 130, and/or the problem146 to determine one or more likely causes 148.

The cause identifier 136 may select sensor data analysis rules and/orthe sensor data analysis models that are configured to receive theenergy usage data 128, the diagnostic data 130, and/or the problem 146.The sensor data analysis rules and/or the sensor data analysis modelsmay be similar to those used by the problem identifier 134 in that theyare configured to analyze the energy usage data 128 and/or thediagnostic data 130. The sensor data analysis rules and/or the sensordata analysis models used by the cause identifier 136 may also beconfigured to analyze the problem 146. In some implementations, thecause identifier 136 may select the sensor data analysis rules and/orthe sensor data analysis models based on the problem 146. These sensordata analysis rules and/or the sensor data analysis models may beconfigured to analyze energy usage data 128 and the diagnostic data 130based on a likely problem 146.

In the example of FIG. 1 and in stage E, the cause identifier 136 mayreceive the problem 146 from the problem identifier 134 indicating thatthe vehicle 104 did not travel far enough for consuming twenty kilowattsof power. The cause identifier 136 may access the energy usage data 128and/or the diagnostic data 130. The cause identifier 136 may select asensor data analysis model that is configured to identify a cause of thevehicle 104 not traveling far enough based on the received power. Theselected sensor data analysis model may be configured to output a likelycause 148 of the problem 146. The cause 148 may be that the regenerativebraking profile does not match the braking patterns of the user 102.This may be because the brake energy recoverer 124 may be expected torecover a certain amount of energy that would allow the vehicle 104 totravel more than one hundred fifty miles on twenty kilowatt hours ofpower.

Additionally, or alternatively, the cause identifier 136 may select asensor data analysis rule that is configured to identify a cause of thevehicle 104 not traveling far enough based on the received power. Theselected sensor data analysis rule may specify various ranges and/orthresholds for various portions of the energy usage data 128 and/or thediagnostic data 130. The selected sensor data analysis rule may specifythat if the route of the vehicle 104 was along the Pecan Street and ElmStreet route, then the brake energy recoverer 124 should be used for atleast thirty percent of the time and the energy recovered should be atleast five kilowatt hours. The selected sensor data analysis rule mayindicate that if either the brake energy recoverer 124 was not used forat least thirty percent of the time or the energy recovered was lessthan five kilowatt hours, then the cause 148 may be that theregenerative braking profile does not match the braking patterns of theuser 102.

The solution identifier 138 may be configured to analyze the energyusage data 128, the diagnostic data 130, the problem 146, and/or thecause 148 using various sensor data analysis rules and/or sensor dataanalysis models. In some implementations, the sensor data analysis rulesand/or sensor data analysis models may specify a solution 150 thatsolves the problem 146 and/or the cause 148. For example, the cause 148may indicate that a light bulb in the passenger area is burned out. Fromthe results of running sensor data analysis rule(s) and/or sensor dataanalysis model(s), the solution identifier 138 may indicate that thesolution to address this cause is to replace the light bulb. Othercauses may not correspond to a precise solution. In this case, thesensor data analysis rules and/or the sensor data analysis models mayanalyze the energy usage data 128, the diagnostic data 130, the problem146, and/or the cause 148 to determine one or more likely solutions 150.

The solution identifier 138 may select sensor data analysis rules and/orthe sensor data analysis models that are configured to receive theenergy usage data 128, the diagnostic data 130, the problem 146, and/orthe cause 148. The sensor data analysis rules and/or the sensor dataanalysis models may be similar to those used by the problem identifier134 and the cause identifier 136 in that they are configured to analyzethe energy usage data 128 and/or the diagnostic data 130. The sensordata analysis rules and/or the sensor data analysis models used by thesolution identifier 138 may also be configured to analyze the problem146 and/or the cause 148. In some implementations, the solutionidentifier 138 may select the sensor data analysis rules and/or thesensor data analysis models based on the problem 146 and/or the cause148. These sensor data analysis rules and/or the sensor data analysismodels may be configured to analyze energy usage data 128 and thediagnostic data 130 with the problem 146 and/or the cause 148 beingknown.

In the example of FIG. 1 and in stage F, the solution identifier 138 mayreceive the cause 148 from the cause identifier 136 indicating that theregenerative braking profile does not match the braking patterns of theuser 102. In some implementations, the solution identifier 138 mayreceive the problem 146 from the cause identifier 136 or the problemidentifier 134. The solution identifier 138 may access the energy usagedata 128 and/or the diagnostic data 130. The solution identifier 138 mayselect a sensor data analysis model that is configured to identify asolution for the cause of the regenerative braking profile not matchingthe braking patterns of the user 102. The selected sensor data analysismodel may be configured to output a solution 150 for the cause 148. Thesolution 150 may be to update the regenerative braking profile 120 to aprofile that matches the driving habits of the user 102.

Additionally, or alternatively, the solution identifier 138 may select asensor data analysis rule that is configured to identify a solution ofthe regenerative braking profile not matching the braking patterns ofthe user 102. The sensor data analysis rule may specify various rangesand/or thresholds for various portions of the energy usage data 128and/or the diagnostic data 130. The selected sensor data analysis rulemay specify that if the regenerative braking profile does not match thebraking patterns of the user 102 and the target energy recovered by thebrake energy recoverer 124 is within fifty percent of the actual brakeenergy recovered, then the solution 150 is to update the regenerativebraking profile 120 to a profile that matches the driving habits of theuser 102.

In stage G, the instruction generator 154 of the server 106 may generateinstructions 152 for the vehicle 104 to implement the solution 150.Depending on the solution 150, the instruction generator 154 may accessadditional data that may be located on another computing device orstored on the server 106. In the example of FIG. 1 , the solutionidentifier 138 generates the solution 150 of updating the regenerativebraking profile 120 to a profile that matches the driving habits of theuser 102. In this case, the instruction generator 154 may access variousregenerative braking profiles to identify the regenerative brakingprofile #456 that corresponds to the user 102. If a regenerative brakingprofile does not correspond to the user 102, then the solutionidentifier 138 may compare the driving habits of the user 102 asreflected in the vehicle sensor data 144 to identify the regenerativebraking profile #456 that matches the driving habits of the user 102.

The instruction generator 154 may provide the instructions 152 to thevehicle 104. The instructions 152 may include the new regenerativebraking profile #456 to begin using with the user 102 driving thevehicle 104. The instructions 152 may instruct the vehicle 104 toreplace the regenerative braking profile 120 with the new regenerativebraking profile #456 included in the instructions 152.

In some implementations, the instructions 152 may instead be arecommendation. In this case, the recommendation may indicate to replacethe regenerative braking profile 120 with the new regenerative brakingprofile #456 included in the recommendation. In some implementations,the recommendation may not include the new regenerative braking profile#456 and may indicate to replace the regenerative braking profile 120with a regenerative braking profile that corresponds to the user 102.

In some implementations, the solution identifier 138 may determinewhether the solution 150 is something that the server 106 canautomatically implement. If the solution 150 is something that theserver 106 can automatically implement, then the server 106 may generateinstructions 152 with which the vehicle 104 or another computing deviceshould comply. For example, updating software, activating hardware,and/or deactivating hardware may be actions that the server 106 canautomatically implement. This may include altering switches in thecharger circuitry 112, altering switches in the receiver circuitry 123,and/or adjusting the wattage of power provided to the vehicle 104 fromthe charging pad 110 or other charging pads. These actions may occur inresponse to the instruction generator 154 transmitting instructions tothe vehicle 104 or the charger circuitry 112 for automaticimplementation. Other actions such as training the user 102 may beactions that the server 106 can recommend but not automaticallyimplement.

In stage H, the vehicle 104 receives the instructions 152. Based on theinstructions 152, the vehicle 104 may update the regenerative brakingprofile 120 with the new regenerative braking profile #456 as specifiedby the instructions 152. In the case where the instructions 152 includea recommendation, the vehicle 104 may output the recommendation to adisplay and/or another computing device such as a mobile phone, laptopcomputer, tablet, and/or any other similar computing device. A user mayview the recommendation and indicate whether the recommendation will beimplemented. The server 106 may receive data indicating whether theinstruction 152 was successfully implemented or whether a user agreed tocomply with the recommendation. The server 106 may store the dataindicating whether the instruction 152 was successfully implemented orwhether a user agreed to comply with the recommendation. The problemidentifier 134, the cause identifier 136, and the solution identifier138 may use that data when identifying subsequent problems, causes, andsolutions.

In some implementations, the charger sensor data 142 may not be directlyprovided to the server 106 from the charger sensors 114 and the vehiclesensor data 144 may not be directly provided to the server 106 from thevehicle 104. The charger sensors 114 may provide the charger sensor data142 to the vehicle 104. The vehicle 104 may provide the charger sensordata 142 and the vehicle sensor data 144 to the server 106.Additionally, or alternatively, the vehicle 104 may provide the vehiclesensor data 144 to the charger circuitry 112 and/or the charger sensors114. The charger circuitry 112 and/or the charger sensors 114 mayprovide the charger sensor data 142 and the vehicle sensor data 144 tothe server 106. In some implementations, the charger sensors 114 and/orthe vehicle 104 may provide the charger sensor data 142 and/or thevehicle sensor data 144 to an intermediate device. The intermediatedevice may provide the charger sensor data 142 and/or the vehicle sensordata 144 to the server 106. The intermediate device may be any type ofdevice that is capable of communicating with the charger sensors 114,vehicle 104, and the server 106. For example, the intermediate devicemay be a mobile phone, tablet, smart watch, laptop computer, desktopcomputer, and/or any other similar device.

FIG. 2 illustrates an example server 200 that is configured to determinethe cause of an issue with a wireless power transfer system or electricvehicle. The server 200 may be any type of computing device that isconfigured to communicate with other computing devices. The server 200may communicate with other computing devices using a wide area network,a local area network, the internet, a wired connection, a wirelessconnection, and/or any other type of network or connection. The wirelessconnections may include Wi-Fi, short-range radio, infrared, and/or anyother wireless connection. The server 200 may be similar to the server106 of FIG. 1 . Some of the components of the server 200 may beimplemented in a single computing device or distributed over multiplecomputing devices. Some of the components may be in the form of virtualmachines or software containers that are hosted in a cloud incommunication with disaggregated storage devices.

The server 200 may include a communication interface 205, one or moreprocessors 210, memory 215, and hardware 220. The communicationinterface 205 may include communication components that enable theserver 200 to transmit data and receive data from other devices andnetworks. In some implementations, the communication interface 205 maybe configured to communicate over a wide area network, a local areanetwork, the internet, a wired connection, a wireless connection, and/orany other type of network or connection. The wireless connections mayinclude Wi-Fi, short-range radio, infrared, and/or any other wirelessconnection.

The hardware 220 may include additional user interface, datacommunication, or data storage hardware. For example, the userinterfaces may include a data output device (e.g., visual display, audiospeakers), and one or more data input devices. The data input devicesmay include, but are not limited to, combinations of one or more ofkeypads, keyboards, mouse devices, touch screens that accept gestures,microphones, voice or speech recognition devices, and any other suitabledevices.

The memory 215 may be implemented using computer-readable media, such asnon-transitory computer-readable storage media. The memory 215 mayinclude a plurality of computer-executable components that areexecutable by the one or more processors 210 to perform a plurality ofactions. Computer-readable media includes, at least, two types ofcomputer-readable media, namely computer storage media andcommunications media. Computer storage media includes volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD), high-definition multimedia/data storage disks, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other non-transmissionmedium that can be used to store information for access by a computingdevice. In contrast, communication media may embody computer-readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave, or other transmissionmechanism.

The memory 215 may store sensor data 225. The sensor data 225 may besimilar to the sensor data of FIG. 1 . Like the sensor data of FIG. 1 ,the sensor data 225 may include energy usage data 230 and diagnosticdata 235. The energy usage data 230 may be similar to the energy usagedata 128 of FIG. 1 , and the diagnostic data 235 may be similar to thediagnostic data 130 of FIG. 1 . The sensor data 225 may store datarelated to multiple vehicles. The communication interface 205 mayreceive data for storage in the sensor data 225. The processor(s) 210may determine whether that data should be stored in the energy usagedata 230 or the diagnostic data 235. In some implementations, the sensordata 225 may not differentiate between the energy usage data 230 or thediagnostic data 235. In this case, the communication interface 205 mayreceive data for storage in the sensor data 225. The processor(s) 210may store that data in the sensor data 225. In some implementations, theprocessor(s) 210 may timestamp the received data to indicate when theserver 200 received the sensor data 225. In some implementations, thereceived data may already be timestamped by the collecting device toindicate the date and time when the data was collected.

The one or more processors 210 may implement the analyzer 255. Theanalyzer 255 may be similar to the analyzer 132 of FIG. 1 . Like theanalyzer 132, the analyzer 255 may be configured to analyze the sensordata 225 using the problem identifier 260, the cause identifier 265, andthe solution identifier 270. The problem identifier 260 may be similarto the problem identifier 134 of FIG. 1 . The cause identifier 265 maybe similar to the cause identifier 136 of FIG. 1 . The solutionidentifier 270 may be similar to the solution identifier 138 of FIG. 1 .The problem identifier 260 may analyze the energy usage data 230 and/orthe diagnostic data 235 related to a vehicle to determine whether thereare any problems with that vehicle. If the problem identifier 260detects a problem, then the cause identifier 265 may analyze theproblem, the energy usage data 230, and/or the diagnostic data 235 todetermine the cause of the problem with the vehicle. The solutionidentifier 270 may analyze the problem, cause, the energy usage data230, and/or the diagnostic data 235 to determine the solution to thecause of the problem with the vehicle. The problem identifier 260, thecause identifier 265, and the solution identifier 270 may use the sensordata analysis models 240 and/or the sensor data analysis rules 245 toanalyze the problem, cause, the energy usage data 230, and/or thediagnostic data 235.

The one or more processors may implement the instruction generator 280.The instruction generator 280 may be similar to the instructiongenerator 154 of FIG. 1 . The instruction generator 280 may receive thesolution from the solution identifier 270. The instruction generator 280may determine whether the solution can be automatically implemented by ahardware or software component of the vehicle or the charging circuitry.For example, if the solution is updating software, then the instructiongenerator 280 can generate the instruction that includes the softwareupdate along with instructions to update the software. If the solutionis altering various switches, then the instruction generator 280 maydetermine whether the switches can be remotely altered. If the switchescan be remotely altered, then the instruction generator 280 may generateand transmit an instruction to alter the switches.

The one or more processors 210 may implement a model trainer 275. Themodel trainer 275 may be configured to train the sensor data analysismodels 240 using machine learning and the historical data 250 andgenerate the sensor data analysis rules 245 using the historical data250. The memory 215 may store the historical data 250. The historicaldata 250 may store data similar to the sensor data 225 that is relatedto various vehicles. Portions of the historical data 250 may includelabels that identify whether the portions are related to a specificproblem, cause, and/or solution. Other portions of the historical data250 may include labels indicating whether the portions are not relatedto a specific problem, cause, and/or solution.

The historical data 250 may be grouped according to vehicle or types ofvehicles. The historical data 250 may include energy usage data 230 anddiagnostic data 235 received from each vehicle. The energy usage data230 and diagnostic data 235 for each vehicle may also be grouped withenergy usage data 230 and diagnostic data 235 received from variouschargers that provided power to the vehicle. The energy usage data 230and diagnostic data 235 received from various chargers may be groupedwith the energy usage data 230 and diagnostic data 235 from a vehiclebefore, during, and after providing power to the vehicle. In someimplementations, the before period may include any period beforecharging the vehicle and after charging another vehicle. The afterperiod may include any period after charging the vehicle and beforecharging another vehicle.

The energy usage data 230 and diagnostic data 235 may include timestampsthat indicate when the corresponding sensors collected the data. Theenergy usage data 230 and diagnostic data 235 may also include datarelated to any identified problem, cause, and/or solution. In someimplementations, the energy usage data 230 and diagnostic data 235 maybe labeled once the problem, cause, and/or solution are known. Theidentification of the problem, cause, and/or solution may come from auser analyzing a previous problem, cause, or solution. For example, thesensors of a vehicle may be providing sensor data to the server 200 forseveral days. After a period, a user may identify a problem with thevehicle. The user may determine a cause of the problem and a solution.With the problem, cause, and solution identified, the user may determinethe period of time when the problem existed. The user or another usermay associate the problem, cause, and solution with the portion of theenergy usage data 230 and diagnostic data 235 for that vehiclecorresponding to the period of time when the problem existed. Portionsof the energy usage data 230 and diagnostic data 235 that do notcorrespond to problem, cause, and solution may be labeled as no problem,cause, and/or solution.

The sensor data 225 may contain various types of data collected fromvehicles and chargers. The sensor data received from a vehicle mayinclude location data, accessory usage data, brake pedal usage data,throttle usage data, steering wheel position data, speed data, passengerload data, battery level data, interior temperature data, motortemperature, receiving pad temperature, battery temperature data for theentire battery and/or for each battery cell, exterior temperature data,battery voltage, volumetric heat generation of the battery,conduction—convection parameter of the battery, Reynolds number of thebattery, an aspect ratio of the battery, battery percentage remaining,energy recovered from a brake energy recoverer, data identifying adriver, data identifying a regenerative braking profile, batterycapacity, wireless energy received, energy stored in the battery,previous charging locations, orientation between charging pad andreceiving pad, distance between charging pad and receiving pad, humiditydata inside the vehicle, humidity data outside the vehicle, humiditydata in or near any portion of the receiving pad, motor, or battery,water presence data inside the vehicle, water presence data outside thevehicle, water presence data in or near any portion of the motor,receiving pad, or battery, and/or any other similar data. Theaccessories may include headlights, air conditioner, heater, interiorlights, defrost, audio players, navigation equipment, door operation,public announcement system, and/or any other similar types ofaccessories. The sensor data received from a charger may includelocation data, data identifying charged vehicles, wireless energyprovided, energy received from a power supply, temperature data inand/or near any portion of the charging pad or charger circuitry,humidity data in and/or near any portion of the charging pad or chargercircuitry, water presence data in and/or near any portion of thecharging pad or charger circuitry, pressure data indicating pressurereceived from any exterior object such as a vehicle, and/or any othersimilar information. Any of the sensor data received from a vehicleand/or the charger may include timestamps that indicate a date and timeduring which the corresponding sensor detected the data. The sensor datareceived from the charger and/or the vehicle may include a subset of thecollected sensor data.

The model trainer 275 may group the historical data 250 into datasamples. Each data sample may represent the state of a vehicle and/or acharger at a point in time. A data sample may include the sensor datacollected from the vehicle at that point in time and sensor datacollected from a charger from which the vehicle is receiving orpreviously received wireless power. Each data sample may also include adata label identifying a problem and/or cause of the problem thatexisted at that time with the vehicle and/or a charger. The data samplemay also include the solution used to correct the problem and/or cause.If the period of time for the data sample does not correspond to aproblem and/or cause, then the data sample may include a data labelindicating that no problem and/or cause exists.

The model trainer 275 may group the data samples into various traininggroups. The model trainer 275 may use the training groups to trainmodels using machine learning. The resulting models may be configured toreceive sensor data and output data indicating a problem, cause, and/orsolution. If there is no problem, cause, and/or solution, then the modelmay output data indicating that there is no problem, cause, and/orsolution. The model trainer 275 may group the data samples into thetraining groups according to various characteristics. For example, themodel trainer 275 may form training groups that include data samples forthe same vehicle, the same vehicle manufacturer, the same model ofvehicle, vehicles with the same type of battery, vehicles with the sametype of receiving pad, the same type of vehicle (e.g., bus, car,motorcycle, etc.), vehicles that charged at the same charger, vehiclesthat charged at the same type of charger, vehicles that charged at acharger with the same type of charging pad, and/or any other similargroup.

The model trainer 275 may include different labels for each data sampledepending on the intended output of the resulting model. For example, ifthe intended output of the model is to identify the problem, the modeltrainer may include the corresponding problem label with each datasample. If the intended output of the model is the cause, then the modeltrainer may include the corresponding cause, and optionally the problem,with each data sample. If the intended output of the model is thesolution, then the model trainer may include the corresponding solutionand optionally problem and/or cause with each data sample.

Each data sample may be cumulative up to the previous charge by thevehicle. In this way, the charging of the vehicle may be time equalszero. A first data sample may include the sensor data collected at thistime and any corresponding labels. A second data sample may representtime equals one and may include the sensor data of the first data sampleand the additional sensor data collected at time equals one. A thirddata sample may represent time equals two and may include sensor data ofthe first data sample, the second data sample, and the additional sensordata collected at time equals two. This pattern may continue until thenext charge. Other events may correspond to a time equals zero event andsubsequent data samples may be cumulative from the time equals zeroevent. Other events may include the start of a route, the start of adriver's shift, the first route of a day, and/or any other similarevent. The corresponding ending event for these events may be the end ofthe route, the end of a driver's shift, and the last route of the day,respectively.

The model trainer 275 may train various models using machine learningand the training groups. The resulting models may be configured toreceive data and output data based on the on the sensor data and labelsincluded in the training group. For example, a first training group mayinclude data identifying wireless energy received from the charger,miles driven, battery percentage, battery voltage, brake energyrecovered, and a problem label. The resulting model trained from thefirst training group may be configured to receive data identifyingwireless energy received from the charger, miles driven, batterypercentage, battery voltage, and brake energy recovered and output dataindicating a problem. A second training group may include dataidentifying wireless energy received from the charger, accessory usage,battery percentage, battery voltage, a problem label, and a solutionlabel. The resulting model trained from the second training group may beconfigured to receive data identifying wireless energy received from thecharger, accessory usage, battery percentage, battery voltage, and aproblem, and output data indicating a solution to the problem.

Each model may be configured to receive and analyze the sensor data 225in a cumulative manner. In this way, a model may not output a likelyproblem, cause, or solution with sensor data received at a single time.The model may output a likely problem, cause, or solution afterreceiving sensor data at multiple points in time. For example, a busdriver may begin a bus route. At the beginning of the bus route, the busmay provide sensor data to the server 200. The problem identifier 260may select a model from the sensor data analysis models 240 based on thetypes of sensor data received. The selected model may not be able todetermine whether there is a problem with one snapshot of data. As thebus continues to provide sensor data along the route, the problemidentifier 260 may continue to provide the new sensor data to theselected model. Once the selected model has enough sensor data togenerate an output, the selected model may output data identifying alikely problem or data indicating there is no problem.

The model trainer 275 may analyze the historical data 250 to identifypatterns for generating the sensor data analysis rules 245. The modeltrainer 275 may identify sensor data patterns that correspond todifferent problems, causes, and/or solutions. These sensor data patternsmay be various ranges, thresholds, and/or other similar comparisonmechanisms to analyze the sensor data 225. For example, the modeltrainer 275 may analyze the historical data 250 and determine that thereis a likely problem with the amount of energy recovered by the brakeenergy recoverer if the amount of brake energy recovered is not at leastone kilowatt hour for each forty miles driven. The model trainer 275 maygenerate a sensor data analysis rule that indicates a problem existswith the brake energy recovery mechanism if the amount of energyrecovered by the brake energy recoverer if the amount of brake energyrecovered is not at least one kilowatt hour for each forty miles driven.The model trainer 275 may analyze the historical data 250 and determinethat if battery level has dropped more than twenty percent, the vehiclehas moved less than fifty miles, and the air conditioner has beenrunning for more than two hours, then there is a problem with the amountof power used by the air conditioner.

In some implementations, the sensor data analysis rules 245 may includeuser-specified rules. These rules may be ones that indicate patterns,thresholds, and/or ranges to identify in the sensor data 225. If thesensor data 225 matches any of those patterns, threshold, and/or ranges,then the rules may specify whether a problem, cause, and/or solution islikely present. For example, a user-specified rule may indicate that ifthe temperature of a battery cell is at least ten degrees warmer thananother battery cell, then there is a problem with the warm batterycell. Another user-specified rule may indicate that if the batteryenergy recoverer has been used for at least one hour and the energyrecovered is less than one kilowatt hour, then there is a problem withthe battery energy recoverer.

In some implementations, the historical data 250 may continue to update.As additional historical data 250 is added, the model trainer 275 maycontinue to generate additional data samples and retrain the sensor dataanalysis models 240, generate additional sensor data analysis rules 245,and/or update the patterns, thresholds, and/or ranges of the sensor dataanalysis rules 245. In some implementations, some of the historical data250 may include data collected from vehicles and/or chargers thatprovided the sensor data 225 after a solution attempted to correct theproblem and/or cause. In this way, the model trainer 275 may retrain thesensor data analysis models 240 using additional sensor data receivedfrom previously analyzed vehicles and/or chargers and problem, cause,and/or solution labels received from users.

FIG. 3 illustrates an example vehicle 300 that is configured todetermine the cause of an issue with the electric vehicle 300. Thevehicle 300 may be any type of electric vehicle that is capable ofcommunicating with other vehicles and/or computing devices. The vehicle300 may communicate with other vehicles and/or computing devices using awide area network, a local area network, the internet, a wiredconnection, a wireless connection, and/or any other type of network orconnection. The wireless connections may include Wi-Fi, short-rangeradio, infrared, and/or any other wireless connection. The vehicle 300may be similar to the vehicle 104 of FIG. 1 . Some of the components ofthe vehicle 300 may be implemented in a single vehicle or distributedover the vehicle 300 and various other devices that may communicate withthe vehicle 300 but may not be included in the vehicle 300 as ittravels. Some of the components may be in the form of virtual machinesor software containers that are hosted in a cloud in communication withdisaggregated storage devices.

The vehicle 300 may include a communication interface 305, one or moreprocessors 310, memory 315, and hardware 320. The communicationinterface 305 may include communication components that enable thevehicle 300 to transmit data and receive data from other devices andnetworks. In some implementations, the communication interface 305 maybe configured to communicate over a wide area network, a local areanetwork, the internet, a wired connection, a wireless connection, and/orany other type of network or connection. The wireless connections mayinclude Wi-Fi, short-range radio, infrared, and/or any other wirelessconnection.

The memory 315 may be implemented using computer-readable media, such ascomputer storage media. Computer-readable media includes, at least, twotypes of computer-readable media, namely computer storage media andcommunications media. Computer storage media includes volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD), high-definition multimedia/data storage disks, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other non-transmissionmedium that can be used to store information for access by a computingdevice. In contrast, communication media may embody computer-readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave, or other transmissionmechanism.

The memory 315 may store sensor data 325. The sensor data 325 may besimilar to the sensor data of FIG. 1 and the sensor data 225 of FIG. 2 .Like the sensor data of FIG. 1 and the sensor data 225 of FIG. 2 , thesensor data 325 may include energy usage data 330 and diagnostic data335. The energy usage data 330 may be similar to the energy usage data128 of FIG. 1 and the energy usage data 230 of FIG. 2 , and thediagnostic data 335 may be similar to the diagnostic data 130 of FIG. 1and the diagnostic data 235 of FIG. 2 . The sensor data 325 may storedata related to the vehicle 300. The sensors 375 may generate sensordata, and the processor(s) 310 may determine whether that data should bestored in the energy usage data 330 or the diagnostic data 335. In someimplementations, the sensor data 325 may not differentiate between theenergy usage data 330 or the diagnostic data 335. In this case, theprocessor(s) 310 may store that data in the sensor data 325. In someimplementations, the processor(s) 310 may timestamp the received data toindicate when the sensors 375 generated the sensor data 325. In someimplementations, the sensor data 325 may already be timestamped by thecorresponding sensor upon collection. In some implementations, thesensor data 325 may also include charger sensor data received fromchargers with which the vehicle 300 interacted. For example, the vehicle300 may receive wireless power from a charger. The charger may alsocommunicate with the vehicle and provide charger sensor data that thevehicle 300 may store in the sensor data 325.

The hardware 320 may include additional user interface, datacommunication, or data storage hardware. For example, the userinterfaces may include a data output device (e.g., visual display, audiospeakers), and one or more data input devices. The data input devicesmay include, but are not limited to, combinations of one or more ofkeypads, keyboards, mouse devices, touch screens that accept gestures,microphones, voice or speech recognition devices, and any other suitabledevices.

The hardware 320 may also include vehicle sensors 375, a receiving pad380, a battery 385, and a brake energy converter 390. The vehiclesensors 375 may be similar to the vehicle sensors 116 of FIG. 1 . Thevehicle sensors 375 may be configured to collect data related to thecharacteristics of the vehicle 300, the various components of thevehicle 300, and the environment in and around the vehicle 300. Thereceiving pad 380 may be similar to the receiving pad 122 of FIG. 1 .The receiving pad 380 may be configured to receive wireless energy froma charging pad of a charger. The battery 385 may be similar to thebattery 118 of FIG. 1 . The battery 385 may store and provide power tothe vehicle 300. The brake energy converter 390 may be similar to thebrake energy recoverer 124 of FIG. 1 . The brake energy converter 390may be configured to convert the kinetic energy of the vehicle intoenergy that can be stored in the battery 385. This slows down thevehicle 300 in the process.

The one or more processors 310 may implement the analyzer 355. Theanalyzer 355 may be similar to the analyzer 132 of FIG. 1 and theanalyzer 255 of FIG. 2 . Like the analyzer 132 and the analyzer 255, theanalyzer 355 may be configured to analyze the sensor data 325 using theproblem identifier 360, the cause identifier 365, and the solutionidentifier 370. The problem identifier 360 may be similar to the problemidentifier 134 of FIG. 1 and the problem identifier 260 of FIG. 2 . Thecause identifier 365 may be similar to the cause identifier 136 of FIG.1 and the cause identifier 265 of FIG. 2 . The solution identifier 370may be similar to the solution identifier 138 of FIG. 1 and the solutionidentifier 270 of FIG. 2 . The problem identifier 360 may analyze theenergy usage data 330 and/or the diagnostic data 335 related to avehicle to determine whether there are any problems with that vehicle300. If the problem identifier 360 detects a problem, then the causeidentifier 365 may analyze the problem, the energy usage data 330,and/or the diagnostic data 335 to determine the cause of the problemwith the vehicle 300. The solution identifier 370 may analyze theproblem, cause, the energy usage data 330, and/or the diagnostic data335 to determine the solution to the cause of the problem with thevehicle 300. The problem identifier 360, the cause identifier 365, andthe solution identifier 370 may use the sensor data analysis models 340and/or the sensor data analysis rules 345 to analyze the problem, cause,the energy usage data 330, and/or the diagnostic data 335.

The memory 315 may store the sensor data analysis models 340 and thesensor data analysis rules 345. The sensor data analysis models 340 maybe similar to the sensor data analysis models 240 of FIG. 2 . The sensordata analysis rules 345 may be similar to the sensor data analysis rules245 of FIG. 2 . The problem identifier 360, the cause identifier 365,and the solution identifier 370 may use the sensor data analysis models340 and the sensor data analysis rules 345 to analyze the sensor data325 in a similar manner to the problem identifier 260, the causeidentifier 265, and the solution identifier 270 using the sensor dataanalysis models 240 and the sensor data analysis rules 245 to analyzethe sensor data 225.

The vehicle 300 may receive the sensor data analysis models 340 and thesensor data analysis rules 345 from a server such as the server 106 ofFIG. 1 and/or the server 200 of FIG. 2 . The server 106 and/or theserver 200 may train and generate the sensor data analysis models 340and the sensor data analysis rules 345. The server 106 and/or the server200 may train and generate the sensor data analysis models 340 and thesensor data analysis rules 345 in part using the sensor data 325 thatthe vehicle 300 provides to the server 106 and/or the server 200. Insome implementations, the server 106 and/or the server 200 may train andgenerate updated sensor data analysis models and sensor data analysisrules. In this case, the server 106 and/or the server 200 may providethe updated sensor data analysis models and sensor data analysis rules.The problem identifier 260, the cause identifier 265, and the solutionidentifier 270 may then use the updated sensor data analysis models 340and the sensor data analysis rules 345.

The memory 315 may store a regenerative braking profile 350. Theregenerative braking profile 350 may be similar to the regenerativebraking profile 120 of FIG. 1 . The regenerative braking profile 350 maystore various regenerative braking profiles. While the vehicle 300 ismoving one of the regenerative braking profiles may be flagged asactive. The brake energy converter 390 may be use the activeregenerative braking profile to determine how to operate. In someimplementations, a regenerative braking profile may be linked to aspecific driver. In this case, the driver may select a regenerativebraking profile linked to the driver. In some instances, the vehicle 300may determine the identity of the driver and select the appropriateregenerative braking profile automatically. The regenerative brakingprofiles 350 may include a default profile that the brake energyconverter 390 may use if the brake energy converter 390 does notdetermine which profile to use. In some implementations, a regenerativebraking profile may be linked to a specific route. In this case, thedriver may select a regenerative braking profile linked to the route. Insome implementations, the vehicle 300 may determine the route based onthe movement and location of the vehicle 300.

Integrating the above components into the vehicle 300 may allow thevehicle 300 to identify problems, causes, and solutions. In instanceswhere the solution is something that can be automatically implemented,the vehicle 300 may automatically implement the solution without userintervention. These solutions may include those that include softwareupdates, adjustments to hardware that can be remotely controlled, and/orany similar changes. The hardware or software may be part of the vehicle300 or part of a charger.

FIG. 4 is a flowchart of an example process 400 for determining thecause of an issue with a wireless power transfer system or electricvehicle. In general, the process 400 analyzes energy usage data anddiagnostic data received from a wireless power transfer system and/or anelectric vehicle. The process 400 determines whether there is a problemwith the wireless power transfer system and/or the electric vehicle. Ifthere is a problem, then the process 400 determines the cause and asolution to correct the cause of the problem. The process 400 mayimplement the solution. The process 400 will be described as beingperformed by the server 106 of FIG. 1 and will include references tocomponents of the FIG. 1 . In some implementations, the process 400 maybe performed by the server 200 of FIG. 2 and/or the vehicle 300 of FIG.3 .

The server 106 receives data related to the energy usage of an electricvehicle 104 (410). The server 106 may receive the data from the vehicle104 and/or the charger that may include a charging pad 110 chargercircuitry 112. The data may be related to an amount of power providedfrom the charger to the vehicle 104, the amount of power consumed by amotor of the vehicle, an amount of power received by the vehicle 104from the charger, an amount of power consumed and/or remaining in thebattery 118 of the vehicle 104, the voltage of the battery 118, distancetraveled by the vehicle 104, accessory usage of the vehicle 104,regenerative braking data, and/or any other similar energy usage relateddata. In some implementations, the vehicle 104 includes a receiving pad122 that received wireless power from the charging pad 110 of thecharger.

Based on the data related to the energy usage of the electric vehicle,the server 106 determines that a problem exists with the electricvehicle 104 (420). The server 106 may use various rules and/or models todetermine whether a problem exists with the vehicle 104. In someimplementations, the problem may be that the vehicle is using too muchpower relative to the distance traveled, the battery power may bedecreasing too quickly, the power output by the charger indicates thatthe battery 118 should be charged more, the power received by thecharger indicates that more power should be output by the charger,and/or any other similar problem. In some implementations, the problemmay be related to something other than a power related problem. Forexample, the problem may be that the interior of the vehicle 104 is toodark, the moisture detected in an are of the vehicle or charger isoutside of an acceptable range, the temperature inside the vehicle 104is outside of an acceptable range, and/or any other similar problem.

In response to determining that that the problem exists with theelectric vehicle, the server 106 accesses diagnostic data of theelectric vehicle 104 (430). In some implementations, the diagnostic datamay include battery temperature data, brake energy recovery data,accessory usage, speed data, tire pressure data, accelerometer data,magnetometer data, location data, light sensor data, gravity sensordata, presence detection data, and/or any other similar type of data. Insome implementations, the server 106 receives the diagnostic data fromthe vehicle 104 periodically, in response to an event, and/or inresponse to a request. The server 106 may store the diagnostic data andaccess the diagnostic data in response to identifying the problem withthe vehicle 104.

Based on the diagnostic data of the electric vehicle and the datarelated to the energy usage of the electric vehicle, the server 106determines a cause of the problem with the electric vehicle 104 (440).The server 106 may use various models and/or rules to analyze thediagnostic data and the energy usage data. In some implementations, themodels may be trained using historical data and machine learning. Themodels may be configured to receive the diagnostic data, the energyusage data, the problem, and/or the cause and output data indicating theproblem, the cause, and/or the solution.

In some implementations, the server 106 identifies the cause of theproblem in a two step approach that involved analyzing differentportions of data. The different portions may including overlapping data.The server 106 may analyze a portion of the data that includes energyusage data that may not include diagnostic data. In this way, the server106 may quickly determine whether the vehicle 104 has a problem withoutanalyzing data that includes both the energy usage data and thediagnostic data. This may speed up the problem identification process.If the server 106 identifies a problem, then the server 106 may analyzethe energy usage data and the diagnostic data to determine the causeand/or a solution.

In some implementations, the server 106 identifies the problem byanalyzing the energy usage data and the diagnostic data. The server 106accesses the energy usage data and the diagnostic data periodically, inresponse to an event, and/or in response to a request. The server 106analyzes the energy usage data and the diagnostic data using the modelsand/or the rules and determines whether there is a problem. Whileanalyzing the energy usage data and the diagnostic data may increase thetime period and computing resources used to identify a likely problem,the accuracy of the problem identification may be improved.

The server 106 provides, for output, data indicating the cause of theproblem with the electric vehicle 104 (450). In some implementations,the server 106 determines a solution to address the cause. The server106 may output data identifying the problem, cause, and/or solution. Theserver 106 may output instructions to address the cause and/or solution.The vehicle 104 and/or another device may receive the instructions. Thevehicle 104 and/or other device may automatically implement theinstructions. In some implementations, the instructions may includeactions for a user, such as the driver, to take. For example, theactions may be for the driver to take additional training for drivingthe vehicle 104.

In some implementations, the server 106 may determine that the cause ofthe problem with the vehicle 104 is that the regenerative brakingprofile of the user 102 does not regenerative braking profile that thevehicle 104 is using. In this case, the server 106 may identify thecorrect regenerative braking profile and instruct the vehicle 104 to usethat regenerative braking profile. In some implementations, the server106 may determine that the cause of the problem is the driving patternsof the user 102. In this case, the server 106 may recommend that theuser 102 drive according to the recommended driving pattern, which mayinclude additional training for the user 102. In some implementations,the server 106 may determine that the cause of the problem is that theauxiliary power load of the vehicle 104 is too high for the usage of thevehicle 104. In this case, the server 106 may instruct the user 102 todecrease the auxiliary power load of the vehicle 104 which may includeactions like turning off the interior lights and passenger airconditioning system when the vehicle 104 is not carrying passengers.

Although a few implementations have been described in detail above,other modifications are possible. In addition, the logic flows depictedin the figures do not require the particular order shown, or sequentialorder, to achieve desirable results. In addition, other acts/actions maybe provided, or acts/actions may be eliminated, from the describedflows, and other components may be added to, or removed from, thedescribed systems. Accordingly, other implementations are within thescope of the following claims.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by a computing device, data related to energy usage of anelectric vehicle; based on the data related to the energy usage of theelectric vehicle, determining, by the computing device, that a problemexists with the electric vehicle; in response to determining that thatthe problem exists with the electric vehicle, accessing, by thecomputing device, diagnostic data of the electric vehicle; based on thediagnostic data of the electric vehicle and the data related to energyusage of the electric vehicle, determining, by the computing device, acause of the problem with the electric vehicle; and providing, foroutput by the computing device, data indicating the cause of the problemwith the electric vehicle.
 2. The computer-implemented method of claim1, wherein: determining the cause of the problem with the electricvehicle comprises determining that a regenerative braking profile of theelectric vehicle does not match a driving pattern of a driver of theelectric vehicle, and the computer-implemented method further comprises:determining, by the computing device, an additional regenerative brakingprofile of the electric vehicle that matches the driving pattern of thedriver of the electric vehicle; and providing, for output by thecomputing device and to the electric vehicle, instructions to update theregenerative braking profile of the electric vehicle to the additionalregenerative braking profile of the electric vehicle.
 3. Thecomputer-implemented method of claim 2, wherein: the regenerativebraking profile specifies to decelerate the electric vehicle based on adistance between the electric vehicle and a predetermined location. 4.The computer-implemented method of claim 1, wherein the electric vehicleincludes a receiving pad that is configured to receive power wirelesslyfrom a charging pad.
 5. The computer-implemented method of claim 1,wherein: determining the cause of the problem with the electric vehiclecomprises determining that driving patterns of a driver of the electricvehicle are the cause of the problem with the electric vehicle, and thecomputer-implemented method further comprises: determining, by thecomputing device, a recommended driving pattern for the driver of theelectric vehicle; and providing, for output by the computing device,instructions for the driver to drive the electric vehicle according tothe recommended driving pattern.
 6. The computer-implemented method ofclaim 1, wherein determining the cause of the problem with the electricvehicle comprises: providing, as an input to a model, the diagnosticdata of the electric vehicle and the data related to the energy usage ofthe electric vehicle; and receiving, from the model, data indicating thecause of the problem with the electric vehicle.
 7. Thecomputer-implemented method of claim 6, comprising: receiving, by thecomputing device, historical data that includes previous data related tothe energy usage of the electric vehicle, previous diagnostic data ofthe electric vehicle, and previous data indicating a cause of a previousproblems of the electric vehicle; and training, by the computing device,the model using the historical data and machine learning.
 8. Thecomputer-implemented method of claim 1, wherein the cause of the problemwith the electric vehicle is an auxiliary power load of the electricvehicle.
 9. A system, comprising: one or more processors; and memoryincluding a plurality of computer-executable components that areexecutable by the one or more processors to perform a plurality ofactions, the plurality of actions comprising: receiving, by a computingdevice, data related to energy usage of an electric vehicle; based onthe data related to energy usage of the electric vehicle, determining,by the computing device, that a problem exists with the electricvehicle; in response to determining that that the problem exists withthe electric vehicle, accessing, by the computing device, diagnosticdata of the electric vehicle; based on the diagnostic data of theelectric vehicle and the data related to energy usage of the electricvehicle, determining, by the computing device, a cause of the problemwith the electric vehicle; and providing, for output by the computingdevice, data indicating the cause of the problem with the electricvehicle.
 10. The system of claim 9, wherein: determining the cause ofthe problem with the electric vehicle comprises determining that aregenerative braking profile of the electric vehicle does not match adriving pattern of a driver of the electric vehicle, and the pluralityof actions further comprising: determining, by the computing device, anadditional regenerative braking profile of the electric vehicle thatmatches the driving pattern of the driver of the electric vehicle; andproviding, for output by the computing device and to the electricvehicle, instructions to update the regenerative braking profile of theelectric vehicle to the additional regenerative braking profile of theelectric vehicle.
 11. The system of claim 10, wherein: the regenerativebraking profile specifies to decelerate the electric vehicle based on adistance between the electric vehicle and a predetermined location. 12.The system of claim 9, wherein the electric vehicle includes a receivingpad that is configured to receive power wirelessly from a charging pad.13. The system of claim 9, wherein: determining the cause of the problemwith the electric vehicle comprises determining that driving patterns ofa driver of the electric vehicle are the cause of the problem with theelectric vehicle, and the plurality of actions further comprising:determining, by the computing device, a recommended driving pattern forthe driver of the electric vehicle; and providing, for output by thecomputing device, instructions for the driver to drive the electricvehicle according to the recommended driving pattern.
 14. The system ofclaim 9, wherein determining the cause of the problem with the electricvehicle comprises: providing, as an input to a model, the diagnosticdata of the electric vehicle and the data related to the energy usage ofthe electric vehicle; and receiving, from the model, data indicating thecause of the problem with the electric vehicle.
 15. The system of claim14, wherein the plurality of actions comprise: receiving, by thecomputing device, historical data that includes previous data related tothe energy usage of the electric vehicle, previous diagnostic data ofthe electric vehicle, and previous data indicating a cause of a previousproblems of the electric vehicle; and training, by the computing device,the model using the historical data and machine learning.
 16. The systemof claim 9, wherein the cause of the problem with the electric vehicleis an auxiliary power load of the electric vehicle.
 17. One or morenon-transitory computer-readable media of a computing device storingcomputer-executable instructions that upon execution cause one or morecomputers to perform acts comprising: receiving, by a computing device,data related to energy usage of an electric vehicle; based on the datarelated to the energy usage of the electric vehicle, determining, by thecomputing device, that a problem exists with the electric vehicle; inresponse to determining that that the problem exists with the electricvehicle, accessing, by the computing device, diagnostic data of theelectric vehicle; based on the diagnostic data of the electric vehicleand the data related to the energy usage of the electric vehicle,determining, by the computing device, a cause of the problem with theelectric vehicle; and providing, for output by the computing device,data indicating the cause of the problem with the electric vehicle. 18.The one or more non-transitory computer-readable media of claim 17,wherein: determining the cause of the problem with the electric vehiclecomprises determining that a regenerative braking profile of theelectric vehicle does not match a driving pattern of a driver of theelectric vehicle, and the acts further comprising: determining, by thecomputing device, an additional regenerative braking profile of theelectric vehicle that matches the driving pattern of the driver of theelectric vehicle; and providing, for output by the computing device andto the electric vehicle, instructions to update the regenerative brakingprofile of the electric vehicle to the additional regenerative brakingprofile of the electric vehicle.
 19. The one or more non-transitorycomputer-readable media of claim 18, wherein: the regenerative brakingprofile specifies to decelerate the electric vehicle based on a distancebetween the electric vehicle and a predetermined location.
 20. The oneor more non-transitory computer-readable media of claim 17, wherein theelectric vehicle includes a receiving pad that is configured to receivepower wirelessly from a charging pad.